In [1]:
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)

/hpc/group/pbenfeylab/ch416/miniconda3/envs/genesys/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
In [2]:
## Conda Env genesys on DCC
print(torch.__version__)
print(sc.__version__) 

1.11.0
1.9.6
In [3]:
## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data¶

In [4]:
with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
#dataset = Root_Dataset(data['X_test'], data['y_test'])
#loader = DataLoader(dataset,
#                         batch_size = batch_size,
#                         shuffle = True, drop_last=True)

X_all = np.vstack((data['X_train'],data['X_val'],data['X_test']))
y_all = pd.concat((data['y_train'],data['y_val'],data['y_test']))
dataset = Root_Dataset(X_all, y_all)
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)
In [5]:
classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}
In [6]:
cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000

Extract a sample¶

In [7]:
sample = next(iter(loader))
xo = sample['x'].to(device)
y = sample['y'].to(device)
y_label = [num2class[i] for i in y.tolist()]
In [8]:
xo.shape
Out[8]:
torch.Size([2000, 11, 17513])
In [9]:
len(y_label)
Out[9]:
2000
In [10]:
len(loader)
Out[10]:
55
In [11]:
## GRN for the transition t5 to t7
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            #pred_h = model.init_hidden(batch_size)
            #tfrom = model.generate_current(x, pred_h, 0).to('cpu').detach().numpy()
            #cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            cfrom = x[np.where(np.array(y_label)==ct)[0],5,:] 
            
            #pred_h = model.init_hidden(batch_size)
            #tto = model.generate_next(x, pred_h, 0).to('cpu').detach().numpy()   
            #cto = tto[np.where(np.array(y_label)==ct)[0],:]
            cto = x[np.where(np.array(y_label)==ct)[0],7,:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast

/tmp/ipykernel_919714/1864579634.py:21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()

Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella
In [12]:
# Save the array to disk
np.save('genesys_raw_ctw_t5-t7.npy', ctw)
In [11]:
ctw = np.load('genesys_raw_ctw_t5-t7.npy')
In [12]:
## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    if np.std(ctw[i])==0:
        ctw_z[i] = np.zeros((17513, 17513))
    else:
        ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])
In [13]:
## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list¶

In [14]:
wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")
In [15]:
## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"
In [16]:
pd.Series(wanted_TFs['Name']).value_counts().head(5)
Out[16]:
Name
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: count, dtype: int64

Network Analysis¶

In [17]:
TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))
In [18]:
def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)
In [19]:
atri = network(0)
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In [20]:
atri
Out[20]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
ARR5 0.499062 0.450281 0.048780 0.923193 0.000305 0.135721
WRKY13 0.110694 0.073171 0.037523 0.913601 0.000223 0.041895
GL2 0.444653 0.181989 0.262664 0.904058 0.000331 0.110958
PHE1 0.082552 0.028143 0.054409 0.881664 0.000203 0.035780
LSMT-L 0.024390 0.005629 0.018762 0.877841 0.000202 0.014887
... ... ... ... ... ... ...
RR12 0.031895 0.003752 0.028143 0.000000 0.000212 0.021155
HMGB1 0.020638 0.001876 0.018762 0.000000 0.000201 0.012669
BEH1 0.016886 0.000000 0.016886 0.000000 0.000210 0.010162
SDG2 0.035647 0.009381 0.026266 0.000000 0.000231 0.023970
MBF1A 0.011257 0.009381 0.001876 0.000000 0.000157 0.009045

534 rows × 6 columns

In [21]:
tri = network(1)
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In [22]:
cor = network(2)
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In [23]:
end = network(3)
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In [24]:
per = network(4)
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In [25]:
pro = network(5)
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In [26]:
xyl = network(6)
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In [27]:
phl = network(7)
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In [28]:
lrc = network(8)
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In [29]:
col = network(9)
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In [30]:
atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']
In [31]:
## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts())
tf_occurance = tf_occurance.rename(columns={
    'count': 'tf_occurance'
})
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)
In [32]:
## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[32]:
atri_betweenness_centrality tri_betweenness_centrality lrc_betweenness_centrality atri_out_centrality tri_out_centrality lrc_out_centrality atri_in_centrality tri_in_centrality lrc_in_centrality centrality_count centrality_sum
In [33]:
## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[33]:
atri_betweenness_centrality tri_betweenness_centrality atri_out_centrality tri_out_centrality atri_in_centrality tri_in_centrality centrality_count centrality_sum
AT2G28710 0.275685 0.000044 0.469043 0.027972 0.232645 0.020979 6 7.026368
WRKY61 0.020229 0.023228 0.356473 0.400932 0.088180 0.109557 6 6.998600
HB17 0.529398 0.000011 0.215760 0.030303 0.095685 0.006993 6 6.878150
AT3G05860 0.057065 0.000060 0.208255 0.020979 0.258912 0.011655 6 6.556925
HB24 0.021784 0.000054 0.322702 0.006993 0.110694 0.060606 6 6.522833
AT5G06800 0.000071 0.002974 0.050657 0.258741 0.016886 0.076923 6 6.406251
RFI2 0.003110 0.000027 0.001876 0.009324 0.026266 0.018648 6 6.059252
In [34]:
## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[34]:
atri_betweenness_centrality atri_out_centrality atri_in_centrality centrality_count centrality_sum
WRKY13 0.913601 0.073171 0.037523 3 4.024295
GL2 0.904058 0.181989 0.262664 3 4.348711
PHE1 0.881664 0.028143 0.054409 3 3.964215
LSMT-L 0.877841 0.005629 0.018762 3 3.902231
AT4G31650 0.773269 0.007505 0.106942 3 3.887715
AT3G13840 0.388579 0.022514 0.026266 3 3.437360
MC2 0.192276 0.183865 0.039400 3 3.415540
MEA 0.123930 0.103189 0.204503 3 3.431622
AT4G18110 0.020987 0.001876 0.037523 3 3.060387
TTG2 0.020158 0.088180 0.225141 3 3.333479
OFP18 0.012371 0.150094 0.258912 3 3.421377
HSFB3 0.009712 0.065666 0.024390 3 3.099769
AT3G21330 0.002335 0.003752 0.009381 3 3.015468
AT1G11490 0.001880 0.001876 0.033771 3 3.037527
MYB97 0.000885 0.001876 0.022514 3 3.025275
NSI 0.000219 0.013133 0.011257 3 3.024609
LBD21 0.000078 0.001876 0.001876 3 3.003830
TRY 0.000004 0.009381 0.080675 3 3.090060
In [35]:
## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[35]:
tri_betweenness_centrality tri_out_centrality tri_in_centrality centrality_count centrality_sum
LRL3 0.788015 0.962704 0.498834 3 5.249553
DAR5 0.623952 0.002331 0.020979 3 3.647262
RSL4 0.584913 0.554779 0.261072 3 4.400764
FRS5 0.327724 0.002331 0.002331 3 3.332386
AT4G09100 0.249815 0.673660 0.272727 3 4.196202
AT2G20030 0.242729 0.076923 0.006993 3 3.326645
RSL2 0.208037 0.682984 0.060606 3 3.951626
AT3G53370 0.106905 0.400932 0.331002 3 3.838840
AT2G24830 0.089569 0.009324 0.041958 3 3.140851
RHD6 0.084281 0.643357 0.372960 3 4.100598
LBD23 0.055149 0.006993 0.011655 3 3.073797
AT2G05160 0.048352 0.254079 0.102564 3 3.404995
AT5G04390 0.034878 0.088578 0.009324 3 3.132780
AT1G02040 0.031109 0.004662 0.013986 3 3.049757
GL3 0.022482 0.060606 0.023310 3 3.106398
RAP2.11 0.007832 0.151515 0.051282 3 3.210629
RR14 0.006781 0.002331 0.016317 3 3.025429
RL6 0.002467 0.011655 0.060606 3 3.074728
GATA26 0.000605 0.025641 0.013986 3 3.040232
bZIP23 0.000163 0.013986 0.027972 3 3.042121
NAGS1 0.000076 0.018648 0.020979 3 3.039703
AT2G04740 0.000060 0.002331 0.013986 3 3.016377
LOL2 0.000022 0.016317 0.027972 3 3.044311
ASG3 0.000022 0.027972 0.027972 3 3.055966
AT2G14760 0.000005 0.023310 0.004662 3 3.027977
In [36]:
## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[36]:
lrc_betweenness_centrality lrc_out_centrality lrc_in_centrality centrality_count centrality_sum
GATA2 0.268995 0.496689 0.290066 3 4.055750
HB4 0.024328 0.037086 0.027815 3 3.089228
GATA11 0.018928 0.010596 0.027815 3 3.057338
AT1G74840 0.008304 0.161589 0.181457 3 3.351350
OFP6 0.005396 0.336424 0.019868 3 3.361688
... ... ... ... ... ...
AT3G24820 0.000005 0.003974 0.005298 3 3.009277
AGL94 0.000005 0.002649 0.025166 3 3.027820
AT4G00270 0.000004 0.005298 0.018543 3 3.023845
AT2G18850 0.000004 0.002649 0.019868 3 3.022520
AT1G50620 0.000002 0.003974 0.021192 3 3.025167

94 rows × 5 columns

In [37]:
## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[37]:
col_betweenness_centrality col_out_centrality col_in_centrality centrality_count centrality_sum
SMZ 0.151406 0.139208 0.029374 3 3.319989
IAA20 0.058495 0.048531 0.208174 3 3.315200
HSFA8 0.052913 0.025543 0.074074 3 3.152530
CHR24 0.000970 0.002554 0.049808 3 3.053333
AT1G50410 0.000746 0.061303 0.029374 3 3.091423
SNI1 0.000477 0.169860 0.035760 3 3.206096
FRS8 0.000418 0.033206 0.030651 3 3.064275
AT1G03150 0.000367 0.002554 0.011494 3 3.014416
NF-YC1 0.000349 0.028097 0.007663 3 3.036109
AT3G13350 0.000314 0.017880 0.006386 3 3.024579
MBF1B 0.000286 0.068966 0.054917 3 3.124168
LD 0.000268 0.012771 0.076628 3 3.089668
HAM1 0.000217 0.010217 0.010217 3 3.020651
AT2G41450 0.000189 0.022989 0.002554 3 3.025732
FRS6 0.000150 0.010217 0.014049 3 3.024416
HAC4 0.000147 0.005109 0.007663 3 3.012918
NTT 0.000124 0.002554 0.083014 3 3.085692
HDG11 0.000124 0.031928 0.062580 3 3.094632
OFP10 0.000105 0.005109 0.001277 3 3.006490
BPC5 0.000103 0.003831 0.001277 3 3.005211
POB1 0.000101 0.012771 0.007663 3 3.020535
AT1G58220 0.000093 0.008940 0.007663 3 3.016696
AT4G36050 0.000075 0.011494 0.024266 3 3.035835
BEH2 0.000072 0.057471 0.051086 3 3.108629
AT5G03220 0.000062 0.019157 0.003831 3 3.023051
HSI2 0.000057 0.021711 0.020434 3 3.042203
AT5G26610 0.000042 0.002554 0.008940 3 3.011537
AGL30 0.000039 0.002554 0.014049 3 3.016642
AT4G25610 0.000038 0.006386 0.003831 3 3.010255
OXS2 0.000036 0.029374 0.007663 3 3.037073
MYB4R1 0.000031 0.010217 0.008940 3 3.019188
AT3G11450 0.000026 0.017880 0.008940 3 3.026846
AT1G77570 0.000026 0.086845 0.001277 3 3.088149
RRS1 0.000024 0.019157 0.025543 3 3.044724
AT3G20800 0.000021 0.015326 0.039591 3 3.054938
HAC1 0.000018 0.029374 0.043423 3 3.072815
AT2G18090 0.000015 0.011494 0.003831 3 3.015340
AT5G64950 0.000013 0.002554 0.015326 3 3.017893
IPT2 0.000011 0.006386 0.001277 3 3.007674
ETL1 0.000010 0.012771 0.012771 3 3.025553
CDC5 0.000010 0.019157 0.025543 3 3.044710
AT1G17520 0.000005 0.008940 0.031928 3 3.040873
ASIL1 0.000005 0.011494 0.003831 3 3.015331
ZML1 0.000005 0.010217 0.033206 3 3.043428
AT4G03250 0.000003 0.003831 0.001277 3 3.005112
ZFWD1 0.000003 0.005109 0.019157 3 3.024269
HD2C 0.000002 0.026820 0.012771 3 3.039593
AT2G21230 0.000002 0.020434 0.044700 3 3.065136
ELO3 0.000002 0.003831 0.014049 3 3.017882
NAC017 0.000002 0.037037 0.020434 3 3.057473
ATO 0.000002 0.012771 0.019157 3 3.031930
In [38]:
## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[38]:
cor_betweenness_centrality end_betweenness_centrality cor_out_centrality end_out_centrality cor_in_centrality end_in_centrality centrality_count centrality_sum
AT1G05710 0.127350 0.410901 0.541414 0.400197 0.197980 0.064897 6 7.742739
RAX2 0.002441 0.647675 0.084848 0.314651 0.078788 0.429695 6 7.558098
BLJ 0.000008 0.886681 0.006061 0.341200 0.018182 0.295969 6 7.548100
MYB12 0.130606 0.111314 0.206061 0.006883 0.779798 0.296952 6 7.531613
AGL42 0.000266 0.911574 0.018182 0.203540 0.066667 0.077679 6 7.277908
ERF15 0.000286 0.943206 0.078788 0.033432 0.052525 0.046214 6 7.154451
JKD 0.070421 0.002084 0.139394 0.127827 0.442424 0.246804 6 7.028954
AT3G24120 0.013839 0.230192 0.254545 0.156342 0.139394 0.191740 6 6.986053
IDD4 0.001333 0.421370 0.195960 0.084562 0.040404 0.037365 6 6.780994
OFP12 0.000008 0.497908 0.002020 0.001967 0.173737 0.027532 6 6.703172
RVN 0.164426 0.352433 0.010101 0.052114 0.034343 0.040315 6 6.653732
GATA27 0.000106 0.306146 0.016162 0.080629 0.016162 0.038348 6 6.457553
AT5G41920 0.000004 0.180894 0.004040 0.028515 0.004040 0.047198 6 6.264692
SCL27 0.000033 0.000170 0.018182 0.020649 0.050505 0.135693 6 6.225232
LCL1 0.000025 0.002687 0.022222 0.063913 0.022222 0.078663 6 6.189732
DBP1 0.000254 0.000031 0.056566 0.008850 0.040404 0.055064 6 6.161168
AT3G61180 0.000589 0.000018 0.042424 0.032448 0.026263 0.013766 6 6.115508
In [39]:
## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[39]:
cor_betweenness_centrality cor_out_centrality cor_in_centrality centrality_count centrality_sum
AT2G38300 0.481495 0.404040 0.442424 3 4.327960
AT2G42660 0.098520 0.191919 0.224242 3 3.514681
AT1G72210 0.059948 0.349495 0.329293 3 3.738736
LRP1 0.012035 0.111111 0.230303 3 3.353449
SIGF 0.008719 0.022222 0.014141 3 3.045082
ESE3 0.005140 0.006061 0.054545 3 3.065747
AT4G20970 0.003165 0.002020 0.016162 3 3.021347
WRKY50 0.002331 0.002020 0.034343 3 3.038695
NAC044 0.002135 0.006061 0.004040 3 3.012236
EPR1 0.001276 0.012121 0.042424 3 3.055821
AT5G55580 0.000642 0.016162 0.002020 3 3.018824
MYB64 0.000188 0.004040 0.016162 3 3.020390
AT5G08430 0.000131 0.020202 0.002020 3 3.022353
ULT1 0.000110 0.006061 0.117172 3 3.123343
AT1G63170 0.000094 0.044444 0.010101 3 3.054640
HMGB2 0.000074 0.026263 0.040404 3 3.066740
AT1G63490 0.000057 0.018182 0.038384 3 3.056623
AT2G37650 0.000008 0.008081 0.012121 3 3.020210
HSFA1D 0.000008 0.006061 0.022222 3 3.028291
ABF3 0.000008 0.004040 0.008081 3 3.012129
ZFN3 0.000004 0.028283 0.012121 3 3.040408
TOE2 0.000004 0.006061 0.014141 3 3.020206
In [40]:
## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[40]:
end_betweenness_centrality end_out_centrality end_in_centrality centrality_count centrality_sum
MYB68 9.802124e-01 0.590954 0.237955 3 4.809121
chr31 9.444067e-01 0.271386 0.271386 3 4.487180
EBS 8.767614e-01 0.007866 0.022616 3 3.907243
AT1G14600 8.295270e-01 0.011799 0.022616 3 3.863942
BIB 8.205952e-01 0.092429 0.062930 3 3.975954
TIFY8 8.073818e-01 0.016716 0.011799 3 3.835897
AT1G24210 7.641783e-01 0.016716 0.012783 3 3.793677
MYB122 5.774007e-01 0.314651 0.046214 3 3.938266
ULT2 5.239966e-01 0.094395 0.036382 3 3.654773
AT3G10040 4.760963e-01 0.027532 0.009833 3 3.513461
SCR 4.342845e-01 0.083579 0.177974 3 3.695838
AT5G45113 3.571712e-01 0.001967 0.006883 3 3.366021
AT4G34290 3.338656e-01 0.004916 0.001967 3 3.340749
NF-YA6 3.078609e-01 0.013766 0.005900 3 3.327527
AGL67 2.296646e-01 0.019666 0.005900 3 3.255230
AT4G00940 2.171171e-01 0.300885 0.013766 3 3.531768
CIA2 1.818243e-01 0.006883 0.012783 3 3.201490
AT3G08020 1.246651e-01 0.025565 0.046214 3 3.196445
ERF10 7.309885e-02 0.022616 0.058997 3 3.154711
4-Sep 7.306305e-02 0.008850 0.020649 3 3.102562
AT4G00390 5.393836e-02 0.001967 0.026549 3 3.082454
AT2G25650 3.125895e-02 0.020649 0.001967 3 3.053874
AT1G68030 2.770519e-02 0.010816 0.012783 3 3.051304
AT3G60670 2.195937e-02 0.011799 0.002950 3 3.036709
TRFL5 9.923815e-03 0.005900 0.002950 3 3.018773
EMB2219 9.136026e-03 0.001967 0.021632 3 3.032735
AT4G38340 8.604704e-03 0.077679 0.005900 3 3.092184
AT3G51180 8.060801e-03 0.003933 0.021632 3 3.033626
AT1G74120 4.417036e-03 0.014749 0.012783 3 3.031949
AT4G08250 4.357033e-03 0.008850 0.027532 3 3.040739
MYB70 3.972816e-03 0.016716 0.017699 3 3.038388
DUO1 2.644028e-03 0.002950 0.012783 3 3.018377
BEH1 1.899790e-03 0.000983 0.038348 3 3.041231
AGL16 1.015222e-03 0.029499 0.033432 3 3.063945
FRS4 8.526313e-04 0.016716 0.015733 3 3.033301
TTR1 7.790785e-04 0.008850 0.003933 3 3.013562
AT3G45880 6.958478e-04 0.014749 0.000983 3 3.016428
ASHH3 5.361609e-04 0.006883 0.007866 3 3.015285
AGL62 1.674293e-04 0.017699 0.013766 3 3.031633
AT3G07500 1.267817e-04 0.039331 0.003933 3 3.043391
AT2G28920 9.871554e-05 0.004916 0.000983 3 3.005998
AT2G47210 4.548657e-05 0.006883 0.010816 3 3.017745
APTX 4.064757e-05 0.006883 0.001967 3 3.008890
AT1G63840 2.516278e-05 0.022616 0.033432 3 3.056072
AT1G61970 1.451699e-05 0.034415 0.006883 3 3.041312
AT2G41710 7.742395e-06 0.003933 0.009833 3 3.013774
NF-YA8 2.903398e-06 0.015733 0.037365 3 3.053100
HAT3.1 9.677994e-07 0.004916 0.004916 3 3.009834
AT1G61980 9.677994e-07 0.016716 0.010816 3 3.027533
AT1G78930 9.677994e-07 0.023599 0.000983 3 3.024583
In [41]:
## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[41]:
per_betweenness_centrality pro_betweenness_centrality xyl_betweenness_centrality phl_betweenness_centrality per_out_centrality pro_out_centrality xyl_out_centrality phl_out_centrality per_in_centrality pro_in_centrality xyl_in_centrality phl_in_centrality centrality_count centrality_sum
ATAUX2-11 0.248159 0.544157 0.674719 0.059681 0.018519 0.591656 0.630435 0.013304 0.010893 0.312263 0.183794 0.017738 12 15.305318
AT1G69580 0.010278 0.734781 0.029629 0.982333 0.090414 0.093552 0.021739 0.322616 0.077342 0.118837 0.029644 0.186253 12 14.697418
AT1G61660 0.141926 0.444208 0.231558 0.031112 0.065359 0.214918 0.001976 0.203991 0.040305 0.413401 0.049407 0.114191 12 13.952352
IAA13 0.024999 0.142825 0.003299 0.030958 0.041394 0.328698 0.124506 0.257206 0.087146 0.270544 0.213439 0.135255 12 13.660269
AT3G20640 0.012619 0.008104 0.001792 0.950984 0.040305 0.080910 0.007905 0.064302 0.051198 0.070796 0.019763 0.090909 12 13.399589
ARF9 0.011501 0.008640 0.000106 0.000070 0.044662 0.036662 0.009881 0.036585 0.004357 0.087231 0.069170 0.035477 12 12.344344
In [42]:
## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[42]:
per_betweenness_centrality per_out_centrality per_in_centrality centrality_count centrality_sum
GATA23 0.981663 0.023965 0.055556 3 4.061184
bZIP4 0.972654 0.214597 0.018519 3 4.205770
FRS11 0.940028 0.005447 0.022876 3 3.968350
AT1G26790 0.924041 0.208061 0.014161 3 4.146263
GAMMA-H2AX 0.920123 0.020697 0.041394 3 3.982214
AT1G27050 0.392489 0.007625 0.018519 3 3.418633
AT4G00238 0.251481 0.002179 0.001089 3 3.254749
WHY3 0.220835 0.005447 0.005447 3 3.231728
AT1G61960 0.219207 0.001089 0.004357 3 3.224654
BSM 0.202307 0.003268 0.013072 3 3.218647
AT1G64620 0.133430 0.044662 0.059913 3 3.238005
AT3G18960 0.099519 0.004357 0.005447 3 3.109323
MGP 0.087651 0.118736 0.096950 3 3.303337
LBD14 0.085349 0.026144 0.188453 3 3.299946
AT2G35430 0.061325 0.072985 0.042484 3 3.176794
OFP1 0.047817 0.049020 0.037037 3 3.133874
IDD11 0.035437 0.038126 0.174292 3 3.247855
AT2G34450 0.022291 0.004357 0.031590 3 3.058239
HB21 0.015165 0.302832 0.059913 3 3.377910
AT1G79220 0.010213 0.004357 0.015251 3 3.029820
AT5G46915 0.003830 0.002179 0.019608 3 3.025616
AT4G12850 0.001089 0.002179 0.006536 3 3.009804
ORC1B 0.001087 0.004357 0.005447 3 3.010891
HAT9 0.000881 0.025054 0.020697 3 3.046633
HDA3 0.000877 0.030501 0.037037 3 3.068415
AT2G47680 0.000498 0.001089 0.003268 3 3.004855
AT3G10030 0.000489 0.004357 0.033769 3 3.038616
PRR3 0.000368 0.002179 0.007625 3 3.010172
AGL26 0.000311 0.017429 0.054466 3 3.072207
SHOT1 0.000233 0.001089 0.005447 3 3.006769
AN3 0.000190 0.001089 0.002179 3 3.003458
AT1G44810 0.000139 0.038126 0.053377 3 3.091642
AT1G21200 0.000099 0.004357 0.001089 3 3.005545
ATWHY2 0.000097 0.039216 0.055556 3 3.094869
MBD02 0.000080 0.028322 0.049020 3 3.077422
BOP2 0.000038 0.009804 0.067538 3 3.077380
MYB65 0.000037 0.014161 0.014161 3 3.028359
AT1G03650 0.000015 0.009804 0.015251 3 3.025070
AT1G05920 0.000010 0.001089 0.002179 3 3.003277
MDA1 0.000010 0.005447 0.029412 3 3.034868
AT3G04850 0.000008 0.016340 0.011983 3 3.028331
NAGS2 0.000008 0.026144 0.021786 3 3.047939
TOP3A 0.000007 0.002179 0.023965 3 3.026151
AT1G09710 0.000001 0.017429 0.002179 3 3.019609
In [43]:
## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[43]:
pro_betweenness_centrality pro_out_centrality pro_in_centrality centrality_count centrality_sum
MYB10 0.979873 0.032870 0.059418 3 4.072162
IAA12 0.976809 0.664981 0.309735 3 4.951524
HAT3 0.858265 0.127686 0.026549 3 4.012500
BZIP24 0.820888 0.021492 0.003793 3 3.846173
HSFB4 0.812082 0.063211 0.032870 3 3.908163
ZFP7 0.786570 0.016435 0.029077 3 3.832082
MYB60 0.765826 0.036662 0.042984 3 3.845472
HB18 0.212028 0.108723 0.102402 3 3.423153
AT1G76870 0.139194 0.006321 0.002528 3 3.148044
BBX30 0.043848 0.021492 0.026549 3 3.091888
CHR18 0.018122 0.013906 0.003793 3 3.035821
bHLH115 0.001740 0.120101 0.108723 3 3.230564
APRR2 0.001659 0.012642 0.008850 3 3.023151
AT5G13780 0.001495 0.005057 0.022756 3 3.029308
RAP2.2 0.000794 0.044248 0.059418 3 3.104460
BIM3 0.000368 0.010114 0.010114 3 3.020596
AT4G25210 0.000155 0.024020 0.036662 3 3.060838
PIE1 0.000130 0.005057 0.017699 3 3.022886
ERF3 0.000077 0.020228 0.055626 3 3.075930
HMGB6 0.000075 0.005057 0.039191 3 3.044323
TOE3 0.000059 0.013906 0.006321 3 3.020287
AT2G43280 0.000054 0.005057 0.040455 3 3.045566
CRF12 0.000050 0.016435 0.015171 3 3.031655
U2AF35B 0.000005 0.013906 0.006321 3 3.020232
In [44]:
## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[44]:
xyl_betweenness_centrality xyl_out_centrality xyl_in_centrality centrality_count centrality_sum
VND1 0.990756 0.373518 0.106719 3 4.470994
HB31 0.990087 0.363636 0.116601 3 4.470324
MYB83 0.989324 0.749012 0.160079 3 4.898415
VND6 0.972661 0.322134 0.059289 3 4.354084
AT1G68200 0.968837 0.505929 0.077075 3 4.551841
AT4G16610 0.961018 0.239130 0.110672 3 4.310821
AT1G66810 0.930619 0.666008 0.084980 3 4.681607
AT4G14490 0.914801 0.003953 0.027668 3 3.946421
VND7 0.706997 0.764822 0.173913 3 4.645732
MYB99 0.416609 0.260870 0.073123 3 3.750601
MYB85 0.371451 0.120553 0.207510 3 3.699515
MYB52 0.330149 0.197628 0.164032 3 3.691809
VND2 0.245791 0.788538 0.498024 3 4.532352
VND5 0.203933 0.677866 0.181818 3 4.063617
MYB46 0.202759 0.867589 0.306324 3 4.376672
VND4 0.139252 0.782609 0.262846 3 4.184706
MED31 0.130599 0.005929 0.017787 3 3.154315
IDD2 0.057778 0.001976 0.003953 3 3.063707
SPL7 0.052929 0.146245 0.096838 3 3.296012
ZHD3 0.030028 0.588933 0.110672 3 3.729633
IAA6 0.029887 0.535573 0.249012 3 3.814472
LBD18 0.029226 0.604743 0.193676 3 3.827645
BAM7 0.017489 0.001976 0.007905 3 3.027371
LBD31 0.010144 0.173913 0.217391 3 3.401448
BZIP49 0.004395 0.126482 0.152174 3 3.283051
AT4G19630 0.000450 0.007905 0.017787 3 3.026142
SAP7 0.000160 0.013834 0.063241 3 3.077236
FBH3 0.000063 0.073123 0.136364 3 3.209549
JAZ12 0.000039 0.112648 0.118577 3 3.231264
AT5G46910 0.000027 0.201581 0.045455 3 3.247063
In [45]:
## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[45]:
phl_betweenness_centrality phl_out_centrality phl_in_centrality centrality_count centrality_sum
AT2G03500 0.884306 0.620843 0.506652 3 5.011800
ARF18 0.830031 0.038803 0.003326 3 3.872160
GATA20 0.589867 0.206208 0.076497 3 3.872572
BHLH101 0.443258 0.043237 0.008869 3 3.495365
AT3G12730 0.109359 0.741685 0.474501 3 4.325545
AT3G46070 0.032355 0.002217 0.016630 3 3.051202
AT5G52010 0.007051 0.014412 0.093126 3 3.114589
CRF1 0.003602 0.084257 0.156319 3 3.244178
ZAP1 0.002910 0.076497 0.070953 3 3.150360
BPC4 0.001725 0.014412 0.019956 3 3.036093
SOL1 0.001421 0.087583 0.065410 3 3.154415
AT4G31420 0.000054 0.023282 0.094235 3 3.117571
REM22 0.000026 0.036585 0.027716 3 3.064327
CHR38 0.000002 0.022173 0.003326 3 3.025501
TCP8 0.000002 0.029933 0.032151 3 3.062087
AT1G48040 0.000001 0.006652 0.016630 3 3.023283
AT1G04850 0.000001 0.017738 0.041020 3 3.058760
In [46]:
tf_spec.to_csv('scRNAseq_TF_GRN_centrality_t5-t7_zscore3.csv', index=True)
In [47]:
tf_spec
Out[47]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_betweenness_centrality atri_closeness_centrality atri_eigenvector_centrality tri_degree_centrality tri_out_centrality tri_in_centrality ... phl_in_centrality phl_betweenness_centrality phl_closeness_centrality phl_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_betweenness_centrality col_closeness_centrality col_eigenvector_centrality
RD26 8.0 0.425891 0.365854 0.060038 0.140364 0.000295 0.118146 0.135198 0.093240 0.041958 ... 0.055432 0.000028 0.000325 0.051446 0.016603 0.011494 0.005109 0.000036 0.000217 0.008457
IAA28 8.0 0.103189 0.084428 0.018762 0.002257 0.000229 0.046224 0.023310 0.011655 0.011655 ... 0.145233 0.005191 0.000408 0.083308 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
RAV1 7.0 0.155722 0.140713 0.015009 0.000000 0.000234 0.063013 0.067599 0.037296 0.030303 ... 0.043237 0.000006 0.000338 0.038996 0.172414 0.085568 0.086845 0.000939 0.000302 0.050019
AT5G51780 7.0 0.037523 0.035647 0.001876 0.003393 0.000217 0.019195 0.034965 0.023310 0.011655 ... 0.074279 0.005337 0.000347 0.042469 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
ARR6 7.0 0.523452 0.499062 0.024390 0.642413 0.000317 0.134664 0.153846 0.118881 0.034965 ... 0.057650 0.003210 0.000343 0.033548 0.033206 0.024266 0.008940 0.000461 0.000184 0.014827
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
ABAP1 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.005543 0.000000 0.000203 0.003816 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
AT4G31060 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.011086 0.000000 0.000229 0.007358 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
TCP24 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.004435 0.000000 0.000195 0.007298 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
PPD1 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.011086 0.000000 0.000250 0.008328 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
NAC016 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.014049 0.007663 0.006386 0.000000 0.000145 0.006568

1430 rows × 61 columns

In [ ]: